Motivation. Whilst many works demonstrate different ways to hide objects from detection by object detectors, there is a lack of research into how we can reliably detect such object hiding attacks. Cao et al. in [1]} showed that model-level defenses (i.e. input transformations and adversarial training) on multi-sensor fusion models to increase their adversarial robustness does not effectively decrease the attack success rates. The authors have also suggested fusing more sensor modalities to increase the robustness, but argue that this approach does not fundamentally address the weakness of using DNN-based object detectors and would still be exploitable. To this end, we develop an object hiding attack detection methodology that leverages a strong physical invariant of a single sensing modality (LiDAR) to provide an orthogonal and reliable detection of objects in a 3D scene, which is robust against object hiding attacks.
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